“How can quants or financial engineers write financial analytics libraries that can be systematically efficiently deployed on an Intel Xeon Phi co-processor or an Intel Xeon multi-core processor without specialist knowledge of parallel programming? A tried and tested approach to obtaining efficient deployment on many-core architectures is to exploit the highest level of granularity of parallelism exhibited by an application. However, this approach may require exploiting domain knowledge to efficiently map the workload to all cores. Using representative examples in financial modeling, this talk will show how the use of Our Pattern Language (OPL) can be used to formalize this knowledge and ensure that the domains of concerns for modeling and mapping the computations to the architecture are delineated. We proceed to describe work in progress on an Intel Xeon Phi implementation of Quantlib, a popular open-source quantitative finance library.”

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